用动态难度筛选数据，让大模型在稀疏奖励下更高效学会推理。
Goldilocks RL: Tuning Task Difficulty to Escape Sparse Rewards for Reasoning
- 教师模型根据学生表现动态选题，确保题目难易适中。
- 在相同算力下，比传统方法提升12.3%的推理准确率。
- 适合训练大型语言模型的复杂推理能力，尤其擅长稀疏反馈场景。
强化学习已成为激发语言模型推理能力的强大范式。然而，依赖稀疏奖励导致该过程极低样本效率，模型需在广阔搜索空间中摸索，仅靠极少反馈。经典课程学习虽通过按复杂度排序数据来缓解此问题，但以往工作多针对小规模数据集，难以适用于现代语言模型训练的大规模场景。此外，特定模型的最佳排序方式往往不明确。为此，我们提出Goldilocks，一种由教师驱动的数据采样策略，旨在预测学生模型对每道题目的难度。教师模型选择对当前学生而言既不太容易也不太困难的问题（符合黄金标准原则），并结合GRPO训练学生。通过持续观察学生在已见样本上的表现，教师能动态适应学生的演进能力。在OpenMathReasoning数据集上，Goldilocks采样策略在相同计算预算下显著提升了使用标准GRPO训练模型的性能。
原文摘要 · Abstract (English)
Reinforcement learning has emerged as a powerful paradigm for unlocking reasoning capabilities in language models. However, relying on sparse rewards makes this process highly sample-inefficient, as models must navigate vast search spaces with minimal feedback. While classic curriculum learning aims to mitigate this by ordering data based on complexity, prior works have primarily targeted small datasets and do not directly transfer to the large-scale settings typical of modern LM training. Furthermore, the right ordering for a specific model is often unclear. To address this, we propose Goldilocks, a novel teacher-driven data sampling strategy that aims to predict each question's difficulty for the student model. The teacher model selects questions of appropriate difficulty for the student model, i.e., questions that are neither too easy nor too hard (Goldilocks principle), while training the student with GRPO. By leveraging the student's performance on seen samples, the teacher continuously adapts to the student's evolving abilities. On the OpenMathReasoning dataset, Goldilocks data sampling improves the performance of models trained with standard GRPO under the same compute budget.
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